Identification Of Malignant Tumors Research Articles

Implementation of a New Classification and Stratification System for Solitary Bone Tumor: Osseous Tumor Radiological and Interpretation and Management System (OT-RIMS)

Abstract Objectives To propose a histological-grades-based Osseous Tumor Radiological Interpretation and Management System (OT-RIMS) that would simplify the radiological evaluation of bone tumors, categorize key radiological features into severity levels, and inform corresponding patient management actions. Methods This retrospective study between January 2015 and August 2022 evaluated patients with solitary bone tumors confirmed by pathology and imaging follow-up received two or three imaging modalities of radiographs, CT, or MRI. Three radiologists independently assessed radiological features, categorized bone lesions based on OT-RIMS criteria, and reached a consensus. Kappa statistics and observed agreement were calculated. Results A total of 341 patients (mean age, 26.0 years; 159 women) were included, with 102 malignant, 177 benign, and 62 intermediate or low-grade malignant bone lesions. Sensitivity and specificity of readers 1, 2, and 3, respectively, in the identification of malignant tumors into OT-RIMS 4 were 93.1% (95 of 102) and 93.3% (223 of 239), 96.1% (98 of 102) and 91.6% (219 of 239), 92.2% (94 of 102) and 89.5% (214 of 239). Inter-reader agreement of OT-RIMS category for three readers was considered excellent (Kendall’s W = 0.924, p < 0.001) with a kappa value of reproducibility in categories 1&2, 3, and 4 of 0.764, 0.528, and 0.930, respectively. Conclusion The OT-RIMS category demonstrated excellent reproducibility despite the reader’s expertise level in categorizing the risk stratification of bone tumors and informing patient management, with histological grades used as the reference standard. Advances in knowledge The OT-RIMS category reliably stratifies bone tumors into four categories corresponding to histological grades and standardized patient management.

Efficient Machine Learning and Deep Learning Techniques for Detection of Breast Cancer Tumor

The detection of cancer tumors is an essential component that has important consequences for the speedy involvement of medical professionals and the enhancement of patient outcomes. This review paper presents a complete study of the current body of research and methodology, as well as an in-depth assessment of the use of machine learning (ML) and deep learning (DL) in the detection of cancer tumors. In addition, the article gives a full analysis of the approaches involved. Machine learning and deep learning, which effectively handle ambiguity in the identification of malignant tumors, provide an alternative method for dealing with the complexity of brain tissue. This method is offered by a combination of machine learning and deep learning. The first part of the review draws attention to the significance of making an accurate diagnosis of breast cancer, highlights the limits of traditional diagnostic methods, and investigates the cutting-edge area of medical imaging technology. After that, it investigates the fundamentals of ML and DL and how they might be used to deal with the challenges that are inherent in the interpretation of complicated imaging data. In addition, the paper explores the ways in which models enhance the processes of feature extraction, picture segmentation, and classification in breast tumor detection systems.

CNT and Graphene-Based Transistor Biosensors for Cancer Detection: A Review.

An essential aspect of successful cancer diagnosis is the identification of malignant tumors during the early stages of development, as this can significantly diminish patient mortality rates and increase their chances of survival. This task is facilitated by cancer biomarkers, which play a crucial role in determining the stage of cancer cells, monitoring their growth, and evaluating the success of treatment. However, conventional cancer detection methods involve several intricate steps, such as time-consuming nucleic acid amplification, target detection, and a complex treatment process that may not be appropriate for rapid screening. Biosensors are emerging as promising diagnostic tools for detecting cancer, and carbon nanotube (CNT)- and graphene-based transistor biosensors have shown great potential due to their unique electrical and mechanical properties. These biosensors have high sensitivity and selectivity, allowing for the rapid detection of cancer biomarkers at low concentrations. This review article discusses recent advances in the development of CNT- and graphene-based transistor biosensors for cancer detection.

Lung nodule classification using combination of CNN, second and higher order texture features

Lung cancer is the most common cancer throughout the world and identification of malignant tumors at an early stage is needed for diagnosis and treatment of patient thus avoiding the progression to a later stage. In recent times, deep learning architectures such as CNN have shown promising results in effectively identifying malignant tumors in CT scans. In this paper, we combine the CNN features with texture features such as Haralick and Gray level run length matrix features to gather benefits of high level and spatial features extracted from the lung nodules to improve the accuracy of classification. These features are further classified using SVM classifier instead of softmax classifier in order to reduce the overfitting problem. Our model was validated on LUNA dataset and achieved an accuracy of 93.53%, sensitivity of 86.62%, the specificity of 96.55%, and positive predictive value of 94.02%.

Ex-Vivo Identification of Tumor From Parenchyma in Human Liver Using Electrochemical Impedance Spectroscopy on a Needle

Percutaneous ethanol injection for hepatocellular carcinoma and biopsy for pathological examination of lesions in the liver are widely performed using a fine needle under ultrasonography guidance. However, it is often difficult to distinguish a malignant tumor from a parenchyma via ultrasonography. This study proposes a method for identifying a malignant tumor in the liver using coplanar bipolar electrodes fabricated on a fine needle, called electrochemical impedance spectroscopy on a needle (EoN). The electrical impedances of the malignant tumor and parenchyma were measured in two pathological statuses, more specifically in human ex-vivo normal liver and in human ex-vivo cirrhotic liver. Statistically significant differences in the electrical impedance between the malignant tumor and parenchyma in each pathological status were observed in a frequency range of 0.46MHz to 1MHz (p < 0.05). Given a real-time measurement in clinical trials, the optimal frequency suggested was indicated by the largest value in the discrimination index between the malignant tumor and the parenchyma. The electrical properties (conductivity and permittivity) of the tissues were estimated to generalize the sensor output without regard to the dimension and shape of the electrodes. The values of the electrical properties of cancer in the cirrhotic liver and normal liver were significantly higher than those of each parenchyma. These experimental results imply that EoN can be used as a potential tool for identification of malignant tumors in the liver during percutaneous ethanol injection and fine needle aspiration procedures.

Association of the primary tumor's SUVmax with survival after surgery for clinical stage IA esophageal cancer: a single-center retrospective study.

Compared to other esophageal cancers, clinical stage IA esophageal cancer generally has a good prognosis, although a subgroup of patients has a poor prognosis. Unfortunately, clinical diagnoses of invasion depth or lymph node metastasis are not always accurate, which make it difficult to identify patients with a high risk of postoperative recurrence using the tumor-node-metastasis staging system. Fluorodeoxyglucose-positron emission tomography may help guide the identification of malignant tumors and the evaluation of their malignant grade based on glucose metabolism. We aimed to evaluate the association between pre-operative fluorodeoxyglucose-positron emission tomography findings and the postoperative prognosis of patients with clinical stage IA esophageal cancer. This single-center retrospective study evaluated pre-esophagectomy fluorodeoxyglucose-positron emission tomography findings from 38 patients with clinical stage IA esophageal cancer. Receiver operating characteristic curve analysis was performed to evaluate the prognostic significance of the primary tumor having low and high SUVmax values (cut-off: 3.56). Overall survival (log-rank p = 0.034) and progression-free survival (log-rank p = 0.008) were significantly different between the groups with low SUVmax values (n = 18) and high SUVmax values (n = 20). Furthermore, the primary tumor's SUVmax value was related to pathological vascular invasion (p = 0.045) and distant metastasis (p = 0.042). The SUVmax of the primary tumor is a predictor of postoperative survival for clinical stage IA esophageal cancer. Thus, using fluorodeoxyglucose-positron emission tomography to evaluate the primary tumor's glucose metabolism may reflect the tumor's grade and potentially compensate for inaccuracies in tumor-node-metastasis staging.

Adaptive Combining via Correlation Exploration for Ultrawideband Breast Cancer Imaging

A novel Adaptive Combining via Correlation Exploration (ACE) algorithm of ultrawideband (UWB) imaging for breast cancer detection is proposed. ACE explores and exploits the correlation between backscattered signals and local coherence reference signals generated within each group of neighboring antennas. High-correlation signals are adaptively selected, summed, and weighted by the product of their corresponding coefficients, forming the intensity of each pixel within an imaging area. The efficacy of proposed algorithm is validated on 3-D anatomically and dielectrically accurate finite-difference time-domain (FDTD) breast models. A variety of scenarios, for both homogenous and heterogeneous, sparse and moderately dense breast models, coupled with both ideal and practical artifact removal methods, are considered. The superior performance of ACE in identification of malignant tumors is demonstrated in comparison to delay-and-sum, delay-multiply-and-sum, and filtered delay-and-sum algorithms. The ACE algorithm is shown to be not only immune to early-stage artifact, but also capable of distinguishing responses from cancerous and fibro-glandular tissues, in which the other three techniques suffer significantly or completely fail. This shows the high promise of ACE for breast cancer screening.

¿Mejora la cirugía mamaria la supervivencia del cáncer oculto de mama?

IntroductionOccult breast cancer (OBC) has an incidence of 0.3-1% but nevertheless represents a diagnostic and therapeutic challenge. The aim of our study was to determine the pathologic characteristics of patients in our OBC series, compare the results of the different diagnostic and therapeutic techniques available (breast surgery with axillary lymphadenectomy or axillary lymphadenectomy alone), and overall survival. Patients and methodsA retrospective analysis was carried out in 21 patients with axillary lymph node metastases and negative mammography and breast ultrasonography. From 2003 onward, our group included magnetic resonance imaging (MRI) in the study protocol of OBC. Overall survival was determined and 2 groups were compared: Group A consisted of patients with OBC treated with breast surgery (n=15), either with radical breast mastectomy (n=10) or breast-conserving surgery (n=5); and group B consisted of patients (n=4) treated surgically with Berg level i or ii axillary lymphadenectomy. We added breast radiotherapy (50Gy) in patients treated with breast-conserving therapy and axillary radiotherapy when there were more than 3 metastatic axillary nodes. ResultsIn 9 patients (7 mastectomies, 2 breast-conserving interventions), one breast carcinoma, 8 infiltrating ductal carcinomas and one medullar carcinoma were identified by histology. Two breast carcinomas were detected by MRI in 6 patients, which were later confirmed by histologic study. The median follow-up was 87 months, with an overall survival of 101.2 months (range: 12-235 months) and a 5-year survival of 69%. The mean survival was 120 months in group A and 41 months in group B (P=.05). ConclusionsIn our series, surgical treatment of OBC improved overall survival compared with lymphadenectomy alone. MRI was useful in the identification of malignant tumors in suspected OBC and allowed patient selection for breast-conserving surgery.